Adapting recommender systems to the requirements of personal health record systems

Wiesner, Martin; Pfeifer, Daniel

doi:10.1145/1882992.1883053

Cited by 46 publications

(31 citation statements)

References 18 publications

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“…It uses profile of users and accordingly the information is provided for the better services of patients. Wiesner et al [122] kept the fact that physical activities are very important for fitness and health, so, they have designed a physical activity recommender system that tells the exercise time useful for people. Recommendation of nutritious diets have been suggested in [123].…”

Section: Hybrid Recommender Systems Based On Integration Of Collaboramentioning

confidence: 99%

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Sohail

Siddiqui

Ali

2017

JESTR

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This paper presents the state of art techniques in recommender systems (RS). The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it focuses on different categories of the recommender systems with deep technical discussions. The review studies on RS are highlighted which helps in understanding the previous review works and their directions. 8 different main categories of recommender techniques and 19 sub categories have been identified and stated. Further, soft computing approach for recommendation is emphasized which have not been well studied earlier. The major problems of the existing area is reviewed and presented from different perspectives. However, solutions to these issues are rarely discussed in the previous works, in this study future direction for possible solutions are also addressed.

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Section: Hybrid Recommender Systems Based On Integration Of Collaboramentioning

confidence: 99%

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Sohail

Siddiqui

Ali

2017

JESTR

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“…The work in [17] present research on integrating personal health data in Personal Health Record (PHR) into Recommender Systems. The approach exploits the existing semantic networks like Wikipedia to obtain a health graph data structure representing health related concepts which can be used to compute the semantic distances among the pairs of such concepts.…”

Section: Literature Survey On Healthcare Recommender Systems (Hrs)mentioning

confidence: 99%

A Survey of Context-Aware Healthcare Recommender Systems

2017

IJSR

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Abstract:The advent of social web has transformed users from sheer consumers to ardent producers of information. The rapid growth coupled with the reliance on the internet for information ranging from e-commerce, e-government and social networks has led to infobesity. Recommender Systems have become essential tool to aid users to overcome the problem of information overload and provide personalized relevant suggestions. The use of Recommender Systems have been successful in both industry and academia. Recommender systems have also found their way into the Healthcare domain with varied applications.Despite the growing practical demand, a few challenges are hindering the adoption ofRecommender Systems in the Healthcare.The purpose of this study is to understand the trend of Recommender Systems applications inHealthcareby examining the published literature, and to provide practitioners and researchers with insight and future direction.We conducted a focused literature review to identify research papers on Recommender Systems in the Healthcare in the last ten years. The key findings of this survey is that the application of Recommender Systems inHealthcare is evolving. The incorporation of contextual information is limited although it has been suggested as a key ingredient to improving the quality of the recommendations and the accuracy of the predications.It is important to point out that aside from the common filtering problems of sparsity, cold start and scalability, a socio-technical issue of privacy, security and trust is emerging.Collaborative Filtering technique is predominantly in use in Healthcare Recommender Systems. Incorporation of contextual information is limited.Privacy, security and trust concerns may hinder the widespread use and acceptance of the Healthcare Recommender Systems.

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“…The semantic models for RS are fast moving in a multidisciplinary way. For example, customer relationship management [41], tourism [42], multimedia [43], travel [37], health domain [44] or [45], games [26], financial [46], social networks [47], e-commerce [48] or media [49] to cite just some of the most relevant cases.…”

Section: Semantic Models For Recommendationmentioning

confidence: 99%

SMORE: Towards a semantic modeling for knowledge representation on social media

Villanueva

González‐Carrasco

López-Cuadrado

et al. 2016

Science of Computer Programming

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This research presents SMORE, a semantic model for knowledge representation on social media. In order to provide recommendations, the model provides the elements for representing the content through the use of an ontological model and semantic techniques for the characterization and relationships between user profiles, products and social networks. In fact, with this model could be the basis of recommendation system based on social media data, and it could be exploited use by the recommendations on different products, which are stored in the Web and with similar characteristics between them. Moreover, SMORE represent the information related to user of the social networks in order to have a user characterization to be used for future recommendations in several domains. The semantic model has been evaluated with semantic data extracted from a trusted social network as, Twitter, obtaining the information specified by an expert in the field of marketing for recommendations in the automotive industry.

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Adapting recommender systems to the requirements of personal health record systems

Cited by 46 publications

References 18 publications

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

A Survey of Context-Aware Healthcare Recommender Systems

SMORE: Towards a semantic modeling for knowledge representation on social media

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